Mark Watkins Big Data Presentation

A PIONEER IN LOCATION SERVICES OUR GPSPublic company: $200M+ revenue, 11 NAVIGATION PARTNERS years in businessLeader in Personalized Mobile Navigation: 30MM+ subscribersLeader in Drive To Mobile Advertising: 750K local advertisersLeader in Mobile Distribution Platforms: 900+ devicesGrowing Global Carrier Audience Reach: 14 carriers in 29 countries 2

KEY PROBLEMS WE ARE WORKING ONTraffic & MappingLocal Search for businesses, events, points of interestLifestyle content & recommendation engineCombination of “traditional” big data processing, machine learning and proprietary algorithmsPeople are drowning in information – use “big data” signals to condense to something manageable

TRAFFIC & MAPSTraffic-aware routing engine – Navigation is core competency – 1.3B routes/trips since 2007Routes generate traffic/motion data – “probe data” from app (billions/month) – Anonymized & summarized to power routing – Persisted in aggregate form for historical traffic metricsUsed to augment Open Street Map – Turn restrictions, stop signs, road geometry – Deduced from probe patternsTechnology set – Hadoop + Hive

AUTOMATED DEVELOPMENT OF RICH LOCAL CONTENT(YOU MAY KNOW THIS AS GOBY) Categorized to taxonomy (“blues”, “hiking trails”) all entities geotagged OTHER FEATURES WORTH NOTING • automatic entity/place creation • aggregated ratings & reviews • proprietary result ranking formula venues automatically recognized; events • domain-specific metadata extraction mapped to venues • sorting by metadata (e.g. price, rating)

AUTOMATED DEVELOPMENT OF RICH LOCAL DATAData space is large, but not immense – Tens or Hundreds of millions (or smaller), not billionsBut very complex – Thousands of data sources – attribute space is 10,000 wide – E.g. how many holes in the golf course; how long is the hiking trail?Generates a large, sparse matrix – Ambiguous, conflicting data – Unstructured or semi-structured data – Need to recognize entities & merge/dedup

SOME LEARNINGSLots of data sources / signals generate “goodness” – Ranking, Confidence, importance, comprehensiveness“Interesting” ≠ “Most Popular”Frequency of occurrence Museum of Bad Art The Middle East NightclubFred’s dry cleaners Museum of Science 2/21/2012 © 2012 Telenav, Proprietary and Confidential 7

RECOMMENDATIONS – WORK IN PROGRESSKey signals – Personalized “interest graph” – “Drive to” data (where are people driving to?) – Entity-level “page rank” – Web/mobile clickstream dataIntegrated with social media – Facebook actions influencing recommendationsKey technology enablers – Large amounts of user-generated data – Proprietary algorithms; machine learning / SVM

Mark Watkins Big Data Presentation

by MassTLC on Feb 21, 2012

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Mark Watkins Big Data Presentation — Presentation Transcript